Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT.
Frontiers in Oncology
OBJECTIVE: To develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR).
METHODS: This study included 100 post-breast-conserving patients with the pCT images, CBCT images, and the target contours, which the physicians delineated. The CT images were generated from CBCT images
RESULTS: The DSC, HD95, and ASD of the target contours with the proposed method were 0.87 ± 0.04, 4.55 ± 2.18, and 1.41 ± 0.56, respectively. Compared with the traditional method without the synthetic CT assisted (0.86 ± 0.05, 5.17 ± 2.60, and 1.55 ± 0.72), the proposed method was outperformed, especially in the soft tissue target, such as the tumor bed region.
CONCLUSION: The CLG model proposed in this study can create the high-quality sCT from low-quality CBCT and improve the performance of DIR between the CBCT and the pCT. The target segmentation accuracy is better than using the traditional DIR.
Li N, Zhou X, Chen S, Dai J, Wang T, Zhang C, et al. Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT. Front Oncol. 2023 Feb 22;13:1127866. doi: 10.3389/fonc.2023.1127866. PMID: 36910636.